This advanced course introduces students to state-of-the-art computational methods for communication research. Building on foundational skills, students learn to apply advanced machine learning and natural language processing techniques to analyze large-scale communication data. Topics include deep learning, transformer models, network analysis, and ethical considerations in contemporary computational research.
A comprehensive, open-source guide to machine learning tailored for communication science researchers and practitioners.
This course introduces students to a foundational understanding of the use of programming languages (largely Python) for computational communication science and the practices behind open science.
The course aims to develop students’ knowledge, understanding, skills, and critical attitudes in the area of digital analytics for communication science and practice. Through hands-on tutorials and practical exercises, students will learn to collect, process, analyze, and visualize digital data using Python programming. Topics covered include Python fundamentals, data manipulation with Pandas, data understanding and privacy, advanced data analysis techniques, A/B testing and experimentation, big data and trace data collection, and large language models.